Goldsmiths Year 2 - Research Methods Practical
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E-mail is the best ways to get in contact with me. I will try to respond to all course-related e-mails within 24 hours. Any questions relating to the course, coursework or anything not personal/confidential in nature should be submitted to the Course Forum. This benefits all students. So, if you email me about the course, don’t be surprised if you get a brief ‘Please post this to the Forum’ message! Office hours are advertised on the module VLE page.
| Instructor | Course |
|---|---|
| Dr. Gordon Wright | Lecture: Mondays |
| Whitehead 200/1 | PSH LG02 10-11am |
| Office Hours: | Labs : Tuesdays |
| g.wright@gold.ac.uk | See personal timetable |
| @DrDeception |
You should plan to attend class in person. I will only open a zoom for virtual attendance on an individual case-by-case basis for specific excusable absences (e.g. illness, family matters, etc.). Lectures will not be recorded. In-person attendance is critical as we will spend a lot of time working on problems and writing code during class. If you have any questions or need special accommodations, send me a message on slack and we can discuss.
Introduction to exploratory data analysis using the R programming language; data visualization, data cleaning, exploratory analysis, information communication, rmarkdown, reproducibility.
Working in and learning a programming language can be as challenging as learning a new spoken language. Hadley Wickham (chief data scientist at RStudio and author of many amazing R packages you’ll be using) made this wise observation:
It’s easy when you start out programming to get really frustrated and think, “Oh it’s me, I’m really stupid,” or, “I’m not made out to program.” But, that is absolutely not the case. Everyone gets frustrated. I still get frustrated occasionally when writing R code. It’s just a natural part of programming. So, it happens to everyone and gets less and less over time. Don’t blame yourself. Just take a break, do something fun, and then come back and try again later.
If you’re finding yourself taking way too long hitting your head against a wall and not understanding, take a break, talk to classmates, ask questions in Slack, and try it again later.
I promise, you can do this
There will be 5 quizzes given about once every other week immediately at the beginning of class. You will not be told in advance when there is a quiz, and make up quizzes will not be available if you miss it (except for excused absences). Please show up on time to class each week to ensure that you do not miss a quiz.
Quizzes will cover material presented in previous classes and assignments during the weeks since the most-recent quiz. Quizzes are short (5-10 minutes) and are designed to test for fluency and to demonstrate where additional study is needed. Quizzes are low-stakes - your worst one is dropped, and the rest count for a small portion of your final grade. If you do poorly on one or two, use that as feedback on where you need additional improvement.
Why quiz at all? Research shows that giving small quizzes throughout a class can dramatically help with retention. It’s a phenomenon known as the “retrieval effect” - basically, you have to practice remembering things, otherwise your brain won’t remember them. The phenomenon and research on it is explained in detail in the book “Make It Stick: The Science of Successful Learning,” by Brown, Roediger, and McDaniel.
Students will be responsible for two types of assignments throughout the semester:
Throughout the semester, students will work in teams of 2-3 students towards a final project of an exploratory data analysis. At the end of the semester, each student will submit a report of their analysis in the form of an html web page and create a 5 to 10-minute video presentation of their results. To make the overall project more manageable, it will be broken down into several separate “milestone” deliverables due throughout the semester, including a proposal, progress report, presentation video, and final report.
View the final project overview page for more details.
Rather than have a final exam, each student will have a 10-minute interview by the instructor. The interview will be focused on the final project the student worked and will contain questions related to concepts covered during the course. Students will be provided a list of questions and the grading rubric ahead of the interview.
Final grades will be calculated as follows:
| Item | Weight | Notes |
|---|---|---|
| Weekly HW | 12 % | |
| Quizzes | 8 % | 5 quizzes, lowest dropped |
| Mini Project 1 | 8 % | Individual assignments |
| Mini Project 2 | 8 % | |
| Mini Project 3 | 8 % | |
| Final Project: Proposal | 9 % | Teams of 2-3 students |
| Final Project: Progress Report | 12 % | |
| Final Project: Report | 16 % | |
| Final Project: Presentation | 9 % | |
| Final Interview | 10 % | Individual interview |
Here’s a visual breakdown by category:
Loading required package: viridisLiteLoading required package: ggplot2
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, union
By the end of the term, you will…
All books are freely available online. Hardcopies are also available for purchase.
| Introduction to Modern Statistics | Çetinkaya-Rundel, Hardin | OpenIntro Inc., 1st Edition, 2021 |